Journal of the American Medical Informatics Association : JAMIA最新文献

{"title":"A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research","authors":"M. Beyene, Philipp A. Toussaint, Scott Thiebes, M. Schlesner, B. Brors, A. Sunyaev","doi":"10.1093/jamia/ocac077","DOIUrl":"https://doi.org/10.1093/jamia/ocac077","url":null,"abstract":"Abstract Objective Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. Materials and Methods We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. Results Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. Discussion Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. Conclusion While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128706046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovating in a crisis: a qualitative evaluation of a hospital and Google partnership to implement a COVID-19 inpatient video monitoring program","authors":"K. Gorbenko, A. Mohammed, Edward Ezenwafor, Sydney Phlegar, P. Healy, Tamara Solly, Ingrid M. Nembhard, Lucy Xenophon, Cardinale B. Smith, R. Freeman, David Reich, Madhu Mazumdar","doi":"10.1093/jamia/ocac081","DOIUrl":"https://doi.org/10.1093/jamia/ocac081","url":null,"abstract":"Abstract Objective To describe adaptations necessary for effective use of direct-to-consumer (DTC) cameras in an inpatient setting, from the perspective of health care workers. Methods Our qualitative study included semi-structured interviews and focus groups with clinicians, information technology (IT) personnel, and health system leaders affiliated with the Mount Sinai Health System. All participants either worked in a coronavirus disease 2019 (COVID-19) unit with DTC cameras or participated in the camera implementation. Three researchers coded the transcripts independently and met weekly to discuss and resolve discrepancies. Abiding by inductive thematic analysis, coders revised the codebook until they reached saturation. All transcripts were coded in Dedoose using the final codebook. Results Frontline clinical staff, IT personnel, and health system leaders (N = 39) participated in individual interviews and focus groups in November 2020–April 2021. Our analysis identified 5 areas for effective DTC camera use: technology, patient monitoring, workflows, interpersonal relationships, and infrastructure. Participants described adaptations created to optimize camera use and opportunities for improvement necessary for sustained use. Non-COVID-19 patients tended to decline participation. Discussion Deploying DTC cameras on inpatient units required adaptations in many routine processes. Addressing consent, 2-way communication issues, patient privacy, and messaging about video monitoring could help facilitate a nimble rollout. Implementation and dissemination of inpatient video monitoring using DTC cameras requires input from patients and frontline staff. Conclusions Given the resources and time it takes to implement a usable camera solution, other health systems might benefit from creating task forces to investigate their use before the next crisis.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123431134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods","authors":"Wenyu Song, Linying Zhang, Luwei Liu, Michael Sainlaire, M. Karvar, Min-Jeoung Kang, A. Pullman, S. Lipsitz, A. Massaro, N. Patil, Ravi Jasuja, P. Dykes","doi":"10.1093/jamia/ocac083","DOIUrl":"https://doi.org/10.1093/jamia/ocac083","url":null,"abstract":"Abstract Objectives The coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19. Methods We screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network. Results All 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults. Conclusions In this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124867564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic blood glucose monitoring impacts on provider and patient behavior","authors":"Allyson Root, Christopher Connolly, S. Majors, Hassan Ahmed, Mattie Toma","doi":"10.1093/jamia/ocac069","DOIUrl":"https://doi.org/10.1093/jamia/ocac069","url":null,"abstract":"Abstract Objective Recent technological development along with the constraints imposed by the coronavirus disease 2019 (COVID-19) pandemic have led to increased availability of patient-generated health data. However, it is not well understood how to effectively integrate this new technology into large health systems. This article seeks to identify interventions to increase utilization of electronic blood glucose monitoring for patients with diabetes. Materials and Methods A large randomized controlled trial tested the impact of multiple interventions to promote use of electronic blood glucose tracking. The total study sample consisted of 7052 patients with diabetes across 68 providers at 20 selected primary care offices. The design included 2 stages: First, primary care practices were randomly assigned to have their providers receive education regarding blood glucose flowsheet orders. Then, patients in the treated practices were assigned to 1 of 4 reminder interventions. Results Provider education successfully increased provider take-up of an online blood glucose monitoring tool by 64 percentage points, while a comparison of reminder interventions revealed that emphasizing accountability to the provider encouraged patients to track their blood glucose online. An assessment of downstream outcomes revealed impacts of the interventions on prescribing behavior and A1c testing frequency. Discussion It is important to understand how health systems can practically promote take-up and awareness of emerging digital health alternatives or those with persistently low utilization in clinical settings. Conclusion These results indicate that provider training and support are critical first steps to promote utilization of patient-generated health data, and that patient communications can provide further motivation.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"336 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123230416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models","authors":"H. E. Echo Wang, M. Landers, R. Adams, Adarsh Subbaswamy, Hadi Kharrazi, D. Gaskin, S. Saria","doi":"10.1093/jamia/ocac065","DOIUrl":"https://doi.org/10.1093/jamia/ocac065","url":null,"abstract":"Abstract Objective Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model’s potential to introduce bias. Materials and Methods Our methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist. Results We selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical ways in which these algorithms can perpetuate health care inequalities. We found that LACE and HOSPITAL have the greatest potential for introducing bias, Johns Hopkins ACG has the most areas of uncertainty, and HATRIX has the fewest causes for concern. Discussion Our approach gives model developers and health care providers a practical and systematic method for evaluating bias in predictive models. Traditional bias identification methods do not elucidate sources of bias and are thus insufficient for mitigation efforts. With our checklist, bias can be addressed and eliminated before a model is fully developed or deployed. Conclusion The potential for algorithms to perpetuate biased outcomes is not isolated to readmission prediction models; rather, we believe our results have implications for predictive models across health care. We offer a systematic method for evaluating potential bias with sufficient flexibility to be utilized across models and applications.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of real-world referential and probabilistic patient matching to advance patient identification strategy","authors":"S. Grannis, Jennifer L. Williams, S. Kasthurirathne, Molly Murray, Huiping Xu","doi":"10.1093/jamia/ocac068","DOIUrl":"https://doi.org/10.1093/jamia/ocac068","url":null,"abstract":"Abstract Objective This study sought both to support evidence-based patient identity policy development by illustrating an approach for formally evaluating operational matching methods, and also to characterize the performance of both referential and probabilistic patient matching algorithms using real-world demographic data. Materials and Methods We assessed matching accuracy for referential and probabilistic matching algorithms using a manually reviewed 30 000 record gold standard reference dataset derived from a large health information exchange containing over 47 million patient registrations. We applied referential and probabilistic algorithms to this dataset and compared the outputs to the gold standard. We computed performance metrics including sensitivity (recall), positive predictive value (precision), and F-score for each algorithm. Results The probabilistic algorithm exhibited sensitivity, positive predictive value (PPV), and F-score of .6366, 0.9995, and 0.7778, respectively. The referential algorithm exhibited corresponding sensitivity, PPV, and F-score values of 0.9351, 0.9996, and 0.9663, respectively. Treating discordant and limited-data records as nonmatches increased referential match sensitivity to 0.9578. Compared to the more traditional probabilistic approach, referential matching exhibits greater accuracy. Conclusions Referential patient matching, an increasingly popular method among health IT vendors, demonstrated notably greater accuracy than a more traditional probabilistic approach without the adaptation of the algorithm to the data that the traditional probabilistic approach usually requires. Health IT policymakers, including the Office of the National Coordinator for Health Information Technology (ONC), should explore strategies to expand the evidence base for real-world matching system performance, given the need for an evidence-based patient identity strategy.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125675577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan","authors":"Prashila Dullabh, Shana Sandberg, Krysta Heaney-Huls, Lauren S. Hovey, D. Lobach, A. Boxwala, P. Desai, E. Berliner, Christine Dymek, M. Harrison, James Swiger, Dean F. Sittig","doi":"10.1093/jamia/ocac059","DOIUrl":"https://doi.org/10.1093/jamia/ocac059","url":null,"abstract":"Abstract Objective We conducted a horizon scan to (1) identify challenges in patient-centered clinical decision support (PC CDS) and (2) identify future directions for PC CDS. Materials and Methods We engaged a technical expert panel, conducted a scoping literature review, and interviewed key informants. We qualitatively analyzed literature and interview transcripts, mapping findings to the 4 phases for translating evidence into PC CDS interventions (Prioritizing, Authoring, Implementing, and Measuring) and to external factors. Results We identified 12 challenges for PC CDS development. Lack of patient input was identified as a critical challenge. The key informants noted that patient input is critical to prioritizing topics for PC CDS and to ensuring that CDS aligns with patients’ routine behaviors. Lack of patient-centered terminology standards was viewed as a challenge in authoring PC CDS. We found a dearth of CDS studies that measured clinical outcomes, creating significant gaps in our understanding of PC CDS’ impact. Across all phases of CDS development, there is a lack of patient and provider trust and limited attention to patients’ and providers’ concerns. Discussion These challenges suggest opportunities for advancing PC CDS. There are opportunities to develop industry-wide practices and standards to increase transparency, standardize terminologies, and incorporate patient input. There is also opportunity to engage patients throughout the PC CDS research process to ensure that outcome measures are relevant to their needs. Conclusion Addressing these challenges and embracing these opportunities will help realize the promise of PC CDS—placing patients at the center of the healthcare system.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131995866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes","authors":"Hossein Estiri, Z. Strasser, S. Rashidian, Jeffrey G. Klann, K. Wagholikar, T. McCoy, S. Murphy","doi":"10.1093/jamia/ocac070","DOIUrl":"https://doi.org/10.1093/jamia/ocac070","url":null,"abstract":"Abstract Objective The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models. Materials and Methods Using data from over 56 000 Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in 4 AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. Models were evaluated both retrospectively and prospectively using model-level metrics of discrimination, accuracy, and reliability, and a novel individual-level metric for error. Results We found inconsistent instances of model-level bias in the prediction models. From an individual-level aspect, however, we found most all models performing with slightly higher error rates for older patients. Discussion While a model can be biased against certain protected groups (ie, perform worse) in certain tasks, it can be at the same time biased towards another protected group (ie, perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. Conclusion Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130629090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer workarounds during the COVID-19 pandemic: analysis and technology implications using the SAMR framework","authors":"Kathleen Yin, Enrico W. Coiera, Joshua Jung, Urvashi Rohilla, A. Lau","doi":"10.1093/jamia/ocac061","DOIUrl":"https://doi.org/10.1093/jamia/ocac061","url":null,"abstract":"Abstract Objective To understand the nature of health consumer self-management workarounds during the COVID-19 pandemic; to classify these workarounds using the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework; and to see how digital tools had assisted these workarounds. Materials and Methods We assessed 15 self-managing elderly patients with Type 2 diabetes, multiple chronic comorbidities, and low digital literacy. Interviews were conducted during COVID-19 lockdowns in May–June 2020 and participants were asked about how their self-management had differed from before. Each instance of change in self-management were identified as consumer workarounds and were classified using the SAMR framework to assess the extent of change. We also identified instances where digital technology assisted with workarounds. Results Consumer workarounds in all SAMR levels were observed. Substitution, describing change in work quality or how basic information was communicated, was easy to make and involved digital tools that replaced face-to-face communications, such as the telephone. Augmentation, describing changes in task mechanisms that enhanced functional value, did not include any digital tools. Modification, which significantly altered task content and context, involved more complicated changes such as making video calls. Redefinition workarounds created tasks not previously required, such as using Google Home to remotely babysit grandchildren, had transformed daily routines. Discussion and Conclusion Health consumer workarounds need further investigation as health consumers also use workarounds to bypass barriers during self-management. The SAMR framework had classified the health consumer workarounds during COVID, but the framework needs further refinement to include more aspects of workarounds.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"409 30","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114008063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for electronic health records phenotyping: A methodical review","authors":"Siyue Yang, Paul Varghese, E. Stephenson, K. Tu, J. Gronsbell","doi":"10.1101/2022.04.23.22274218","DOIUrl":"https://doi.org/10.1101/2022.04.23.22274218","url":null,"abstract":"Objective: Accurate and rapid methods for phenotyping are a prerequisite to realizing the potential of electronic health records (EHRs) data for clinical and translational research. This study reviews the literature on machine learning (ML) approaches for phenotyping with respect to the phenotypes considered, the data sources and methods used, and the contributions within the wider context of EHR-based research. Materials and Methods: We searched for relevant articles in PubMed and Web of Science published between January 1, 2018 and April 14, 2022. After screening, we collected data on 52 variables across 106 selected articles. Results: ML-based methods were developed for 156 unique phenotypes, primarily using EHR data from a single institution or health system. 72 of 106 articles leveraged unstructured data in clinical notes. In terms of methodology, supervised learning is the most prevalent ML paradigm (n = 64, 60.4%), with half of the articles employing deep learning. Semi-supervised and weakly-supervised approaches were applied to reduce the burden of obtaining gold-standard labeled data (n = 21, 19.8%), while unsupervised learning was used for phenotype discovery (n = 20, 18.9%). Federated learning has been applied to develop algorithms across multiple institutions while preserving data privacy (n = 2, 1.9%). Discussion While the use of ML for phenotyping is growing, most articles applied traditional supervised ML to characterize the presence of common, chronic conditions. Conclusion: Continued research in ML-based methods is warranted, with particular attention to the development of advanced methods for complex phenotypes and standards for reporting and evaluating phenotyping algorithms.","PeriodicalId":236137,"journal":{"name":"Journal of the American Medical Informatics Association : JAMIA","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125816291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}